How DSP Outperforms ARM in Certain Fields

Most embedded beginners know that Texas Instruments’ C2000 series DSPs are more suitable for applications in inverters and video processing than the STM32 series based on the Cortex-M core represented by STMicroelectronics. On paper, the Cortex-M series may have a clock frequency 2-4 times that of DSP chips, so what advantages does DSP have to remain undefeated in certain fields? This article will explain through theoretical analysis, practical test cases, and a summary of final results.

Hardware Architecture Optimization: Designed for Real-Time Control

(1) High-Performance Mathematical Computation Capability

  • C2000 DSP uses dedicated DSP + Floating Point Unit (FPU), suitable for high-precision mathematical calculations (such as FFT, PID, PWM modulation).

  • Cortex-M (such as M4/M7) has an FPU, but the DSP instruction set is weaker (e.g., M4 only supports basic MAC operations), making mathematical computation efficiency inferior to C2000.

(2) High-Precision PWM and ADC

  • C2000 provides nanosecond-level PWM resolution (e.g., 150ps high-precision PWM), suitable for the precise timing requirements of inverters and motor control.

  • Cortex-M typically has a PWM resolution of tens of nanoseconds, which is difficult to meet the fine control of high-frequency switch power supplies.

  • C2000’s ADC sampling rate is higher (e.g., 3-5 MSPS), and supports hardware oversampling (to improve signal-to-noise ratio), suitable for real-time feedback control.

(3) Dedicated Peripherals (CLB, CMPSS, HRPWM)

  • Configurable Logic Block (CLB): Allows users to customize digital logic for hardware-level real-time response (e.g., dead-time control, fault protection).

  • High-Resolution PWM (HRPWM): Provides finer duty cycle adjustment than ordinary MCUs (e.g., 0.1% step).

  • Comparator Subsystem (CMPSS): Hardware implementation of fast overcurrent/overvoltage protection (without CPU intervention).

Real-Time Performance: Low Latency & Determinism

(1) Extremely Short Interrupt Response Time

  • C2000 has an interrupt latency as low as tens of nanoseconds, suitable for high-frequency inverter control (e.g., 100kHz+ switching frequency).

  • Cortex-M typically has interrupt response times of 100ns~1μs, which may introduce jitter in high-frequency applications.

(2) Deterministic Execution

  • C2000 has a pipeline and cache design optimized for real-time performance, ensuring that critical tasks (such as PWM generation) are not affected by other tasks.

  • Cortex-M may introduce unpredictable delays during complex task scheduling (e.g., RTOS multitasking).

Software Ecosystem: Optimized for Power Electronics

(1) Dedicated Libraries Provided by TI

  • Digital Power SDK, MotorControl SDK: Provide pre-optimized inverter and motor control algorithms (e.g., PFC, SVPWM).

  • C2000’s CLA (Control Law Accelerator): Can process control algorithms in parallel, freeing up CPU resources.

(2) Direct Support for MATLAB/Simulink

  • Can directly generate code through TI C2000 Hardware Support Package to accelerate motor control and inverter development.

  • Although Cortex-M also supports Simulink, its optimization for power electronics algorithms is not as deep as that of C2000.

Typical Application Comparison

Application Scenario C2000 DSP Advantages Cortex-M Limitations
Photovoltaic Inverter High-precision PWM, fast fault protection, MPPT algorithm optimization Insufficient PWM resolution, lower ADC sampling rate
Motor Control Hardware SVPWM, FOC algorithm acceleration, low-latency current loop Requires external driver chip, poorer real-time performance
Digital Power Nanosecond-level PWM adjustment, multi-channel ADC synchronous sampling Difficult to achieve high-frequency LLC resonant control
Video Processing Dedicated DSP accelerates image algorithms (e.g., edge detection) No dedicated DSP, relies on software implementation, low efficiency

Although C2000 excels in the field of power electronics, Cortex-M is more popular in the following scenarios:

  1. Cost-Sensitive Applications: Cortex-M is cheaper (e.g., STM32F4), suitable for simple motor control.

  2. General Embedded Development: Cortex-M has a broader ecosystem (e.g., Arduino, RT-Thread support).

  3. Low-Power Scenarios: Some Cortex-M chips (e.g., M0+) have lower power consumption than C2000.

Test Results

The following test results are based on the relevant code in the Test Cases section of this article. During actual testing, the corresponding interfaces need to be replaced with their respective platform’s mathematical libraries or hardware acceleration interfaces to better simulate the support provided by each platform’s resources for actual computations.

It can be seen that despite a 2.4 times difference in clock frequency, the DSP’s operational capability is not inferior to that of ARM.

Test Item Execution Time/US Remarks
STM32H743 (Clock Frequency 480M) TMS32C28377D (Clock Frequency 200MHz)
Mathematical Square Root Test 0.3 2.025
Floating Point Addition Test 8.5 12.5 NUM_OPERATIONS == 100
Floating Point Subtraction Test 8.5 12.005 NUM_OPERATIONS == 100
Floating Point Multiplication Test 8.5 12.005 NUM_OPERATIONS == 100
Floating Point Division Test 8.5 13.01 NUM_OPERATIONS == 100
FFT Test 1910 781.72 4096 points
Sine Function Test 0.8 0.165
Cosine Function Test 0.8 0.145
Tangent Function Test 1 1.645

Test Cases

Trigonometric Function Calculation Test Case

#include <stdio.h>
#include <math.h>

#define PI 3.14159265358979323846

// Function to test trigonometric functions
void test_trigonometric_functions() {
    double angle_degrees = 45.0;
    double angle_radians = angle_degrees * PI / 180.0;

    // Test sine function
    double sin_result = sin(angle_radians);
    printf("sin(%lf degrees) = %lf\n", angle_degrees, sin_result);

    // Test cosine function
    double cos_result = cos(angle_radians);
    printf("cos(%lf degrees) = %lf\n", angle_degrees, cos_result);

    // Test tangent function
    double tan_result = tan(angle_radians);
    printf("tan(%lf degrees) = %lf\n", angle_degrees, tan_result);
}

int main() {
    test_trigonometric_functions();
    return 0;
}

Floating Point Calculation Test Case

Performing 100 calculations for floating point addition/subtraction/multiplication/division to test the time consumption for floating point calculations.

#include <stdio.h>
#include <time.h>

#define NUM_OPERATIONS 100

// Floating point addition test
double test_addition() {
    clock_t start, end;
    double cpu_time_used;
    double a = 1.1, b = 2.2, result;
    int i;

    start = clock();
    for (i = 0; i < NUM_OPERATIONS; i++) {
        result = a + b;
    }
    end = clock();
    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
    return cpu_time_used;
}

// Floating point subtraction test
double test_subtraction() {
    clock_t start, end;
    double cpu_time_used;
    double a = 3.3, b = 1.1, result;
    int i;

    start = clock();
    for (i = 0; i < NUM_OPERATIONS; i++) {
        result = a - b;
    }
    end = clock();
    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
    return cpu_time_used;
}

// Floating point multiplication test
double test_multiplication() {
    clock_t start, end;
    double cpu_time_used;
    double a = 2.0, b = 3.0, result;
    int i;

    start = clock();
    for (i = 0; i < NUM_OPERATIONS; i++) {
        result = a * b;
    }
    end = clock();
    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
    return cpu_time_used;
}

// Floating point division test
double test_division() {
    clock_t start, end;
    double cpu_time_used;
    double a = 4.0, b = 2.0, result;
    int i;

    start = clock();
    for (i = 0; i < NUM_OPERATIONS; i++) {
        result = a / b;
    }
    end = clock();
    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
    return cpu_time_used;
}

int main() {
    double addition_time, subtraction_time, multiplication_time, division_time;

    addition_time = test_addition();
    subtraction_time = test_subtraction();
    multiplication_time = test_multiplication();
    division_time = test_division();

    printf("Floating point addition %d operations took: %f seconds\n", NUM_OPERATIONS, addition_time);
    printf("Floating point subtraction %d operations took: %f seconds\n", NUM_OPERATIONS, subtraction_time);
    printf("Floating point multiplication %d operations took: %f seconds\n", NUM_OPERATIONS, multiplication_time);
    printf("Floating point division %d operations took: %f seconds\n", NUM_OPERATIONS, division_time);

    return 0;
}

Square Root Test Case

This example tests the runtime sequence by selecting data of different floating-point precisions for square root calculations.

#include <stdio.h>
#include <math.h>

// Function to test square root function
void test_square_root() {
    double numbers[] = {4.0, 9.0, 16.0, 25.0, 0.25, 0.01, 100.5, 123.456};
    int size = sizeof(numbers) / sizeof(numbers[0]);

    for (int i = 0; i < size; i++) {
        double result = sqrt(numbers[i]);
        printf("sqrt(%lf) = %lf\n", numbers[i], result);
    }
}

int main() {
    test_square_root();
    return 0;
}

FFT Calculation Test Case

The number of test points in this example is 4096. In actual testing, the FFT calculation interface can be replaced with the respective platform’s software processing function or hardware acceleration interface.

#include <stdio.h>
#include <math.h>

#define N 4096  // Number of sampling points, must be a power of 2

// Complex structure
typedef struct {
    double real;
    double imag;
} Complex;

// Complex multiplication
Complex complex_multiply(Complex a, Complex b) {
    Complex result;
    result.real = a.real * b.real - a.imag * b.imag;
    result.imag = a.real * b.imag + a.imag * b.real;
    return result;
}

// Fast Fourier Transform
void fft(Complex *x) {
    int j = 0;
    for (int i = 0; i < N - 1; i++) {
        if (i < j) {
            Complex temp = x[i];
            x[i] = x[j];
            x[j] = temp;
        }
        int k = N / 2;
        while (k <= j) {
            j -= k;
            k /= 2;
        }
        j += k;
    }

    for (int s = 1; s <= log2(N); s++) {
        int m = 1 << s;
        Complex wm;
        wm.real = cos(2 * M_PI / m);
        wm.imag = -sin(2 * M_PI / m);
        for (int k = 0; k < N; k += m) {
            Complex w;
            w.real = 1.0;
            w.imag = 0.0;
            for (int j = 0; j < m / 2; j++) {
                Complex t = complex_multiply(w, x[k + j + m / 2]);
                Complex u = x[k + j];
                x[k + j].real = u.real + t.real;
                x[k + j].imag = u.imag + t.imag;
                x[k + j + m / 2].real = u.real - t.real;
                x[k + j + m / 2].imag = u.imag - t.imag;
                w = complex_multiply(w, wm);
            }
        }
    }
}

// Test FFT function
void test_fft() {
    Complex x[N];
    // Generate test signal
    for (int i = 0; i < N; i++) {
        x[i].real = sin(2 * M_PI * 1 * i / N) + sin(2 * M_PI * 2 * i / N);
        x[i].imag = 0.0;
    }

    // Execute FFT
    fft(x);

    // Output partial results
    for (int i = 0; i < 10; i++) {
        printf("X[%d] = (%lf, %lf)\n", i, x[i].real, x[i].imag);
    }
}

int main() {
    test_fft();
    return 0;
}

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